Papers by Hiroya Takamura
One-class Text Classification with Multi-modal Deep Support Vector Data Description (2021.eacl-main)
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| Challenge: | Using multi-modal deep SVDD, we can build a much better description for target one-class data. |
| Approach: | They propose to extend uni-modal SVDD to multiple modal mSVDD and introduce a mechanism for incorporating negative supervision in the absence of real negative data. |
| Outcome: | The proposed model outperforms uni-modal SVDD and can get further improvements when negative supervision is incorporated. |
BENNERD: A Neural Named Entity Linking System for COVID-19 (2020.emnlp-demos)
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| Challenge: | a biomedical entity linking system is available for COVID-19 research. |
| Approach: | They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base. |
| Outcome: | The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries. |
The Emergence of High-Level Semantics in a Signaling Game (2024.starsem-1)
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| Challenge: | a symbol grounding problem has been raised in recent years in AI . we show that neural agents can communicate high-level semantic concepts . |
| Approach: | They propose to use an adversarial agent to train neural agents in a signaling game . they show that the agents can communicate high-level semantic concepts rather than low-level features . |
| Outcome: | The proposed method can learn to communicate high-level semantic concepts . it also produces an appropriate training signal when no other method is available . |
Pointing to Subwords for Generating Function Names in Source Code (2020.coling-main)
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| Challenge: | Existing methods for generating function names from source code face difficulties in generating low-frequency or out-of-vocabulary subwords. |
| Approach: | They propose two strategies for copying low-frequency or out-of-vocabulary subwords in inputs. |
| Outcome: | The proposed method improves on the Java-small and Java-large datasets and improves the existing method on the GitHub platform. |
Assessing the Belief Consistency of Large Language Models on the Logical Conversation Process (2026.acl-long)
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Tomoki Tsujimura, Matīss Rikters, Masaki Asada, Shusaku Egami, Tatsuya Ishigaki, Ken Yano, Hiroya Takamura
| Challenge: | Large language models have been shown remarkable ability to understand given contexts. |
| Approach: | They propose a method to evaluate whether beliefs held by LLMs remain consistent . they propose to use multiple choice question answering format to assess belief consistency . |
| Outcome: | The proposed method evaluates the consistency of LLMs in a multiple-choice question answering format. |
Aspect-based Analysis of Advertising Appeals for Search Engine Advertising (2022.naacl-industry)
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| Challenge: | ad creators must consider various aspects of advertising appeals such as price, product features, and quality in their ac work. |
| Approach: | They propose to use a dataset of ad texts to explore the effective aspects of advertising appeals (A3) for different industries to assist a search engine ap creators. |
| Outcome: | The proposed model can detect aspects of ad texts and help them estimate their performance. |
Learning to Select, Track, and Generate for Data-to-Text (P19-1)
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
| Challenge: | Existing models often refer to the same data record multiple times. |
| Approach: | They propose a data-to-text generation model with two modules, one for tracking and the other for text generation. |
| Outcome: | The proposed model outperforms existing models even without writer information in all evaluation metrics and contributes to content planning and surface realization. |
Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matrices (2025.coling-main)
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| Challenge: | Existing methods to analyze word sense proportions are insufficient for understanding semantic shifts . et al., 2018: semantic shift and its effects. |
| Approach: | They propose a framework for how semantic shifts occur over multiple time periods by using word embeddings. |
| Outcome: | The proposed framework can analyze semantic shifts over multiple time periods using word embeddings. |
Neural Machine Translation Incorporating Named Entity (C18-1)
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| Challenge: | Conventional NMT models have difficulty translating words with multiple meanings because of the high ambiguity. |
| Approach: | They propose a neural machine translation model that incorporates named entity (NE) tags of source-language sentences to reduce the difficulty in translating multiple meanings. |
| Outcome: | The proposed model achieves 3.11 point improvement in bilingual evaluation understudy (BLEU) on English-to-Japanese translation task with the ASPEC, and English- to-Bulgarian and English to-Romanian translation tasks with the Europarl corpus. |
Neural text normalization leveraging similarities of strings and sounds (2020.coling-main)
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| Challenge: | Existing methods that ignore the similarities of word strings and sounds do not account for these features. |
| Approach: | They propose a neural model that considers the similarities of both word strings and sounds, and a model that takes only the similarity of word strings or of sounds as a baseline. |
| Outcome: | The proposed models outperformed a baseline model and achieved state-of-the-art results on WNUT-2015. |
Variance Matters: Detecting Semantic Differences without Corpus/Word Alignment (2023.emnlp-main)
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| Challenge: | a new method for finding semantic differences in words appears in two corpora, but it requires a variance of word vectors . a word covers more meanings in a corpus, and its mean word vector becomes shorter . |
| Approach: | They propose a method to measure the coverage of meanings of a word in a corpus through the norm of its mean word vector. |
| Outcome: | The proposed methods rival the best-performing system in the SemEval-2020 Task 1 . they are robust for the skew in corpus sizes and capable of detecting infrequent words . |
AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset (2025.findings-acl)
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| Challenge: | Identifying factors that make ad text attractive is essential for advertising success . identifying the linguistic factors presents a significant challenge because of the intricate interplay between the semantic content and its linguistic expression. |
| Approach: | They propose to use a dataset for ad text paraphrasing that contains human preference data to enable analysis of linguistic factors. |
| Outcome: | The proposed dataset is 20 times larger than v1.0 and contains 16,460 pairs of ad text paraphrase pairs . it shows that human preference and ade- t attractiveness are related . |
An empirical analysis of existing systems and datasets toward general simple question answering (2020.coling-main)
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| Challenge: | evaluators of simple factoid question answering using different datasets are not able to solve SimpleQuestions. |
| Approach: | They evaluate the progress of the field toward solving simple factoid questions over a knowledge base. |
| Outcome: | The proposed model is nearly solved on the most popular dataset, but not on the robustness of existing systems. |
Prompting for Numerical Sequences: A Case Study on Market Comment Generation (2024.lrec-main)
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| Challenge: | Large language models have been applied to data-to-text generation tasks, but their effectiveness is limited to tasks where the input data is structured and their components are represented as words. |
| Approach: | They propose to use large language models to generate text from numerical sequences. |
| Outcome: | The proposed models perform better than natural languages and longer formats, while resembling natural languages yield less effective results. |
An Empirical Study of Generating Texts for Search Engine Advertising (2021.naacl-industry)
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| Challenge: | Existing studies on neural language generation have not evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. |
| Approach: | They propose to integrate a reinforcement learning framework into an end-to-end sequence-tosequence (Seq2S) model and demonstrate how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads. |
| Outcome: | The proposed method improves the ads’ impact, deploys the models to a product, and evaluates the generated ads. |
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning (2022.emnlp-main)
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| Challenge: | Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. |
| Approach: | They propose a novel Story Evaluation method that mimics human preference when judging a story . the model is based on a well-annotated dataset and a longformer-encoder-decoder . |
| Outcome: | The proposed method is applicable to machine-generated and human-written stories. |
Disease Network Constructor: a Pathway Extraction and Visualization (2023.acl-demo)
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Mohammad Golam Sohrab, Khoa Duong, Goran Topić, Masami Ikeda, Nozomi Nagano, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Itoh, Hiroya Takamura
| Challenge: | In the human body, various substances (entities) such as proteins and compounds interact and regulate each other, forming huge pathway networks. |
| Approach: | They present a system that extracts and visualizes a disease network derived through regulation events found in scientific articles on idiopathic pulmonary fibrosis. |
| Outcome: | The proposed system extracts and visualizes a disease network from biomedical articles on idiopathic pulmonary fibrosis (IPF) it includes two-dimensional (2D) and 3D visualizations of the constructed disease network. |
Term-Driven Forward-Looking Claim Synthesis in Earnings Calls (2024.lrec-main)
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| Challenge: | Existing arguments synthesis models excel in summarizing arguments, but lack accurate forward-looking perspectives. |
| Approach: | They propose a task called "forward-looking claim planning" that incorporates forward-looking perspectives. |
| Outcome: | The proposed method improves the existing models and improves performance. |
SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2021.findings-emnlp)
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| Challenge: | Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. |
| Approach: | They propose a task of context-aware text generation in the scientific domain to exploit the contributions of context in generated texts. |
| Outcome: | The proposed dataset comprehensively benchmarks the efficacy of the proposed dataset in generating description and paragraph. |
An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models (2020.acl-main)
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| Challenge: | Neural language models are often trained on positive examples, but recent studies suggest they are not robust enough to handle complex syntactic constructions. |
| Approach: | They propose to use negative examples to boost models' robustness on English sentences with a negligible loss of perplexity. |
| Outcome: | The proposed model is robust to negative examples in English with negligible loss of perplexity . |
The Impact of Language on Arithmetic Proficiency: A Multilingual Investigation with Cross-Agent Checking Computation (2024.naacl-short)
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| Challenge: | Large language models (LLMs) have garnered significant attention over the past year . previous studies have evaluated LLMs' performance in solving math word problems, but there is little discussion on whether they comprehend the operations they generate. |
| Approach: | They challenge the notion that arithmetic is language-independent and compare models with cross-agent collaborations to find significant limitations in their performance. |
| Outcome: | The proposed model outperforms collaborative approaches in basic arithmetic tasks. |
Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
| Outcome: | The proposed framework lacks fidelity to the table contents and is based on a pre-trained model and a copy mechanism. |
BiomedCurator: Data Curation for Biomedical Literature (2022.aacl-demo)
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Mohammad Golam Sohrab, Khoa N.A. Duong, Ikeda Masami, Goran Topić, Yayoi Natsume-Kitatani, Masakata Kuroda, Mari Nogami Itoh, Hiroya Takamura
| Challenge: | BiomedCurator uses state-of-the-art natural language processing techniques to extract structured data from scientific articles. |
| Approach: | They propose a web application that extracts structured data from PubMed and ClinicalTrials.gov . the application uses a combination of natural language processing techniques and a pattern-based extraction approach . |
| Outcome: | The proposed system extracts the structured data from PubMed and ClinicalTrials.gov datasets. |
Fiction-Writing Mode: An Effective Control for Human-Machine Collaborative Writing (2023.eacl-main)
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| Challenge: | Large-scale pre-trained language models (PLMs) have demonstrated an exceptional aptitude for generating text with an exceptional degree of fluency and structure. |
| Approach: | They propose to integrate writing skills curricula into human-machine collaborative writing scenarios by adding writing modes as a control for text generation models. |
| Outcome: | The proposed model can be used to generate narrative fiction with a high level of accuracy and similarity with the professionally written target story. |
Metric-Type Identification for Multi-Level Header Numerical Tables in Scientific Papers (2021.eacl-main)
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| Challenge: | Numerical tables are used to present experimental results in scientific papers. |
| Approach: | They propose a task to extract metric-types from multi-level header numerical tables . they propose two joint-learning neural classification and generation schemes . |
| Outcome: | The proposed models handle in-header and out-of-headers metric-type identification problems. |
GADFA: Generator-Assisted Decision-Focused Approach for Opinion Expressing Timing Identification (2025.coling-main)
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| Challenge: | Existing models generate text on demand, but in real-life situations, individuals do not continuously generate text or voice opinions. |
| Approach: | They propose a novel task to identify news-triggered opinion expressing timing by using a dataset generated by professional stock analysts. |
| Outcome: | The proposed model can generate opinion on stock analysts' actions and improves performance in various opinion understanding tasks. |
Who Said What: Formalization and Benchmarks for the Task of Quote Attribution (2024.lrec-main)
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| Challenge: | Existing methods for quote attribution are poorly understood, despite advances in research . previous approaches have used hand-crafted features to identify speaker names . |
| Approach: | They formalize the task of quote attribution and establish a basis for comparison . they compare CEQA and ChatGPT models on available datasets in both English and Chinese . |
| Outcome: | The proposed model outperforms all supervised methods on English and Chinese datasets. |
Exploring the Influence of Spelling Errors on Lexical Variation Measures (C18-1)
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| Challenge: | Lexical richness measures such as Type-Token Ratio and Yule's K are often used for learner English analysis and assessment but are unstable because of spelling errors. |
| Approach: | They propose to use a dictionary to calculate the difference between TTR and Yule’s K caused by spelling errors and to deepen the understanding of the influence of spelling errors on them. |
| Outcome: | The proposed measures are based on English learner corpora of three groups and estimate their values before and after spelling errors are manually corrected. |
Revisiting Statistical Laws of Semantic Shift in Romance Cognates (2022.coling-1)
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| Challenge: | Despite their shared etymology, some cognate pairs have experienced semantic shift. |
| Approach: | They examine the relationship between lexical semantic shift and six intra-linguistic variables, such as frequency and polysemy, and examine the effect of morphologically complex etyma on semantic shift. |
| Outcome: | The results show that frequency and polysemy have positive effects on semantic shift and that morphologically complex etyma are more resistant to it. |
Progressive Visual Refinement for Multi-modal Summarization (2026.eacl-short)
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| Challenge: | Multi-modal summarization (MMS) is a critical research area driven by the proliferation of multimedia content. |
| Approach: | They propose a patch-refined visual information network to exploit multimodal information . they propose combining visual information with textual information to generate concise summaries . |
| Outcome: | Extensive experiments on two public MMS datasets show the superiority of the proposed model. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
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| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
Introducing Spatial Information and a Novel Evaluation Scheme for Open-Domain Live Commentary Generation (2024.findings-emnlp)
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| Challenge: | Compared to domain-specific work in this task, this task proved particularly challenging due to the absence of domain- specific features. |
| Approach: | They propose an utterance generation model with a novel spatial graph that integrates spatial information to deal with the open-domain characteristics of the commentaries and significantly improves performance. |
| Outcome: | The proposed model significantly improves performance in the open-domain live commentary generation task. |
A Neural Model for Aggregating Coreference Annotation in Crowdsourcing (2020.coling-main)
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| Challenge: | Existing studies of natural language labelling tasks have shown that crowd-sourced labels can be noisy. |
| Approach: | They split the aggregation into mention classification and coreference chain inference tasks to predict the correct labels. |
| Outcome: | The proposed model predicts the class of each mention using an autoencoder while taking into account the mention’s annotation complexity and annotators’ reliability at different levels. |
Cross-lingual and Word-Independent Methods for Quantifying Degree of Grammaticalization (2026.eacl-long)
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Ryo Nagata, Daichi Mochihashi, Misato Ido, Yusuke Kubota, Naoki Otani, Yoshifumi Kawasaki, Hiroya Takamura
| Challenge: | Existing methods for quantifying the degree of grammaticalization are language- and word-dependent . existing methods are language dependent and lack training data . |
| Approach: | They propose to use Positive-Unlabeled learning or Cross-Validation-like learning to quantify degree of grammaticalization. |
| Outcome: | The proposed method achieves high correlations to human judgments in English deverbal prepositions and Japanese nouns being grammaticalized. |
Memory-efficient Temporal Moment Localization in Long Videos (2023.eacl-main)
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| Challenge: | Temporal Moment Localization is a multi-modal task that requires understanding the temporal relationships in the entire input video. |
| Approach: | They propose a stochastic sampling module that can process long videos at a constant memory footprint. |
| Outcome: | The proposed model can process videos as long as 18 minutes at a constant memory footprint and achieves faster and faster results than competing models. |
Infinite SCAN: An Infinite Model of Diachronic Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods for capturing semantic changes using word embeddings cannot account for existence of each sense and its relative importance. |
| Approach: | They propose a Bayesian model that can estimate the number of senses of words and their changes through time using a dynamic topic model and a logistic stick-breaking process. |
| Outcome: | The proposed model outperforms the baseline model and investigates the semantic changes of several well-known target words using the CCOHA corpus. |
Demonstration Selection Strategies for Numerical Time Series Data-to-Text (2024.findings-emnlp)
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| Challenge: | Demonstration selection is a critical step in in-context learning, where a prompt is fed into large language models. |
| Approach: | They propose to use sequence similarity-based selection and task-specific knowledge-based demonstration selection methods to select similar instances from an example bank. |
| Outcome: | The proposed methods outperform baseline selections and often surpass fine-tuned models on two benchmark datasets and human judges confirm their performance. |
Learning with Contrastive Examples for Data-to-Text Generation (2020.coling-main)
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Yui Uehara, Tatsuya Ishigaki, Kasumi Aoki, Hiroshi Noji, Keiichi Goshima, Ichiro Kobayashi, Hiroya Takamura, Yusuke Miyao
| Challenge: | Existing models for data-to-text generation generate fluent but sometimes incorrect sentences . Existing studies show that using contrastive examples improves the ability of generating sentences with better lexical choice without degrading the fluency. |
| Approach: | They propose to use models trained on incorrect sentences and learning methods that exploit contrastive examples to reduce such errors. |
| Outcome: | The proposed models generate fluent sentences but often have problematic ones in terms of correctness. |
AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts (2025.findings-naacl)
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| Challenge: | Effective linguistic choices that attract potential customers play crucial roles in advertising success. |
| Approach: | They propose to use a paraphrase dataset to explore linguistic features of ad texts that influence human preferences to maximize the potential success of advertisements. |
| Outcome: | The proposed model improves the attractiveness of ad texts by focusing on human preferences. |
Can GPT-4 Sway Experts’ Investment Decisions? (2025.findings-naacl)
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| Challenge: | In the post-Turing era, evaluating large language models involves assessing generated text based on readers’ decisions rather than merely its indistinguishability from human-produced content. |
| Approach: | They propose to use GPT-4 to evaluate generated text from the aspects of grammar, convincingness, logical coherence, and usefulness to determine its validity. |
| Outcome: | The proposed model can generate persuasive analyses affecting the decisions of amateurs and experts. |
Open-domain Video Commentary Generation (2022.emnlp-main)
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Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura
| Challenge: | Existing approaches to generate live commentary on specific domains have been limited. |
| Approach: | They propose to generate live commentary from transcribed videos in an open-domain setting . they propose to use well-known neural architectures to build models based on transcriptions . |
| Outcome: | The proposed model is based on well-known neural architectures and based off existing models. |
Live Football Commentary System Providing Background Information (2025.acl-demo)
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Yuichiro Mori, Chikara Tanaka, Aru Maekawa, Satoshi Kosugi, Tatsuya Ishigaki, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | Existing studies on sports commentary generation focus on describing major events in the video, but real-world commentary often includes background information. |
| Approach: | They developed an audio commentary system that generates utterances with background information and play-by-play commentary for football matches. |
| Outcome: | The proposed system generates utterances with background information and play-by-play commentary for football matches. |
Towards Parameter-Efficient Integration of Pre-Trained Language Models In Temporal Video Grounding (2023.findings-acl)
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Erica Kido Shimomoto, Edison Marrese-Taylor, Hiroya Takamura, Ichiro Kobayashi, Hideki Nakayama, Yusuke Miyao
| Challenge: | Recent studies have improved query inputs with pre-trained language models, but the effects of this integration are unclear. |
| Approach: | They propose to integrate query sentences with pre-trained language models to train TVG models. |
| Outcome: | The proposed model integrates query sentences with pre-trained language models at cost of more expensive training. |
Coreference Resolution in Full Text Articles with BERT and Syntax-based Mention Filtering (D19-57)
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| Challenge: | Existing systems for coreference resolution are difficult because of their long coreferent chains. |
| Approach: | They propose to use an existing span-based neural coreference resolution system as a baseline . they filter noisy mentions based on parse trees and integrate a highly expressive language model into the system . |
| Outcome: | The proposed system outperforms the baseline system on the CRAFT Shared Tasks 2019 task. |
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models (D19-57)
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| Challenge: | NER and concept indexing perform named entity recognition and concept identifiers (CUIs) in a knowledge base. |
| Approach: | They propose a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI) they use bi-LSTM to capture the semantic information of a sequence and classify them into entities or no entities . |
| Outcome: | The proposed approach performs named entity recognition (NER) and concept indexing (CI) which links them to concept unique identifiers (CUIs) in a knowledge base. |
Generating Weather Comments from Meteorological Simulations (2021.eacl-main)
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Soichiro Murakami, Sora Tanaka, Masatsugu Hangyo, Hidetaka Kamigaito, Kotaro Funakoshi, Hiroya Takamura, Manabu Okumura
| Challenge: | generating weather-forecast comments from meteorological simulations is labor intensive and requires a solid knowledge of meteorology. |
| Approach: | They propose a data-to-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data. |
| Outcome: | The proposed model performs best against baselines in terms of informativeness . it is available online and the results are available to the general public . |
A Simple and Effective Usage of Word Clusters for CBOW Model (2020.aacl-main)
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| Challenge: | Existing word clustering algorithms can be used to obtain word embeddings without additional language resources. |
| Approach: | They propose to replace infrequent input and output words with clusters to produce word embeddings. |
| Outcome: | The proposed method produces embeddings of frequent words and small amount of cluster embeddables, which can be fine-tuned on downstream tasks. |
A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions (2024.lrec-main)
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| Challenge: | Linguistic studies have revealed important aspects of grammaticization of deverbal prepositions. |
| Approach: | They propose a computational approach to measure the degree of grammaticization of deverbal prepositions based on corpus data. |
| Outcome: | The proposed method correlates well with human judgements and supports previous findings in linguistics. |
Improving Numeracy by Input Reframing and Quantitative Pre-Finetuning Task (2023.findings-eacl)
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| Challenge: | Innumeracy is a problem in pretrained language models, but it is not discussed in this paper . Numerals are an indispensable part of narratives and provide much fine-grained information. |
| Approach: | They propose a method to solve innumeracy in pretrained language models by exploring the notation of numbers. |
| Outcome: | The proposed method improves performance in three benchmark datasets containing quantitative-related tasks. |
Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (P19-1)
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| Challenge: | Numeracy is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Approach: | They propose to use a dataset to test whether neural network models can learn numeracy . numerability is the ability to predict the magnitude of a numeral at some specific position in a text description. |
| Outcome: | The proposed task can predict the magnitude of a numeral at a specific position in a text description. |
DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning (2024.findings-acl)
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| Challenge: | Question answering (QA) is a fundamental task in the field of Natural Language Processing (NLP). |
| Approach: | They propose a database querying and reasoning dataset for question answering that is designed to accommodate sequential questions and multi-hop queries. |
| Outcome: | The proposed dataset better mirrors the dynamics of real-world information retrieval and analysis with a particular focus on the financial reports of US companies. |